论文标题
PREF：可预测性正规神经运动场
PREF: Predictability Regularized Neural Motion Fields
论文作者
论文摘要
了解动态场景中的3D运动对于许多视觉应用至关重要。最近的进步主要集中在估计某些特定元素（如人类）的活动上。在本文中，我们利用神经运动场来估计多视图中所有点的运动。由于颜色相似的颜色点和颜色的点的歧义，从动态场景中对动态场景进行建模运动很具有挑战性。我们建议将估计运动的正规化为可预测。如果已知来自先前帧的运动，则应在不久的将来进行运动。因此，我们通过首先调节潜在嵌入的估计运动来引入可预测性正则化，然后通过采用预测器网络来在嵌入式上执行可预测性。所提出的框架pref（可预测性正规字段）比基于最新的神经运动场的动态场景表示方法取得了PAR或更好的结果，同时不需要对场景的先验知识。
Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene.
